Investigating collaborative learning success with physiological coupling indices based on electrodermal activity

Pijeira‐Díaz, Héctor J.; Drachsler, Hendrik; Järvelä, Sanna; Kirschner, Paul A.

doi:10.1145/2883851.2883897

Cited by 57 publications

(44 citation statements)

References 44 publications

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“…And although the recent year has seen learning analytics researchers contributing to this field by combining log data with data from biophysical sensors (e.g. [42]), addressing and taking into account these issues in the current paper would have been out of the scope of our study.…”

Section: Discussionmentioning

confidence: 99%

Widget, Widget on the Wall, Am I Performing Well at All?

Scheffel

Drachsler

Kraker

et al. 2017

IEEE Trans. Learning Technol.

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Abstract-In collaborative learning environments, students work together on assignments in virtual teams and depend on each other's contribution to achieve their learning objectives. The online learning environment, however, may not only facilitate but also hamper group communication, coordination and collaboration. Group awareness widgets that visualise information about the different group members based on information collected from the individuals can foster awareness and reflection processes within the group. In this paper, we present a formative data study about the predictive power of several indicators of an awareness widget based on automatically logged user data from an online learning environment. In order to test whether the information visualised by the widget is in line with the study outcomes, we instantiated the widget indicators with data from four previous runs of the European Virtual Seminar on Sustainable Development (EVS). We analysed whether the tutor gradings in these previous years correlated with the students' scores calculated for the widget indicators. Furthermore, we tested the predictive power of the widget indicators at various points in time with respect to the final grades of the students. The results of our analysis show that the grades and widget indicator scores are significantly and positively correlated, which provides a useful empirical basis for the development of guidelines for students and tutors on how to interpret the widget's visualisations in live runs.

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Section: Discussionmentioning

confidence: 99%

Widget, Widget on the Wall, Am I Performing Well at All?

Scheffel

Drachsler

Kraker

et al. 2017

IEEE Trans. Learning Technol.

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“…Alzoubi, D'Mello, and Calvo () used the combination of EEG, ECG, and galvanic skin response to detect naturalistic expressions of affect. EDA was used by Pijeira‐Díaz et al () in combination with BVP, heart rate, skin temperature, and pupil size. Heart rate has been used by Di Mitri et al () to predict Flow in combination with steps and activity data.…”

Section: Literature Surveymentioning

confidence: 99%

From signals to knowledge: A conceptual model for multimodal learning analytics

Mitri

Schneider

Specht

et al. 2018

Computer Assisted Learning

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Multimodality in learning analytics and learning science is under the spotlight. The landscape of sensors and wearable trackers that can be used for learning support is evolving rapidly, as well as data collection and analysis methods. Multimodal data can now be collected and processed in real time at an unprecedented scale. With sensors, it is possible to capture observable events of the learning process such as learner's behaviour and the learning context. The learning process, however, consists also of latent attributes, such as the learner's cognitions or emotions. These attributes are unobservable to sensors and need to be elicited by human-driven interpretations. We conducted a literature survey of experiments using multimodal data to frame the young research field of multimodal learning analytics. The survey explored the multimodal data used in related studies (the input space) and the learning theories selected (the hypothesis space). The survey led to the formulation of the Multimodal Learning Analytics Model whose main objectives are of (O1) mapping the use of multimodal data to enhance the feedback in a learning context; (O2) showing how to combine machine learning with multimodal data; and (O3) aligning the terminology used in the field of machine learning and learning science. KEYWORDSlearning analytics, machine learning, multimodal data, multimodality, sensors, social signal processing | INTRODUCTIONWith the rise of data-driven techniques to discover insights and generate predictions from the learning process such as learning analytics, the need for 360°data about learners has grown consistently. Combining data coming from multiple sources has become a prominent necessity in learning research and has led to an increased interest in multimodality and consequently into multimodal data analysis. To clarify the concept of multimodality, we use the definition provided by Nigay and Coutaz. The term "multi" refers to "more than one", whereas the term "modal" stands both for "modality" and for "mode". The modality is the type of communication channel used by two agents to convey and acquire information that defines the data exchange.The mode is the state that determines the context in which the information is interpreted (Nigay & Coutaz, 1993). The reasons why multimodality in learning is drawing so much attention can be summarized according to four developments.First of all, multimodality is a consolidated theory. It has been subjected of investigation already for two decades in different fields including functional linguistic, conversational analysis, and social semiotics (Jewitt, Bezemer, & O'Halloran, 2016). Research in multimodal interaction investigated how different modalities interact andThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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“…al [22] who used a mutimodal data for Computer Supported Collaborative Learning in a school setting. Although not focused on using machine learning, the link made with psychophysiology theory introduce a novel research question, i.e.…”

Section: Related Workmentioning

confidence: 99%

Learning pulse

Mitri

Scheffel

Drachsler

et al. 2017

Proceedings of the Seventh International Learning Analytics &Amp; Knowledge Conference

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Learning Pulse explores whether using a machine learning approach on multimodal data such as heart rate, step count, weather condition and learning activity can be used to predict learning performance in self-regulated learning settings. An experiment was carried out lasting eight weeks involving PhD students as participants, each of them wearing a Fitbit HR wristband and having their application on their computer recorded during their learning and working activities throughout the day. A software infrastructure for collecting multimodal learning experiences was implemented. As part of this infrastructure a Data Processing Application was developed to pre-process, analyse and generate predictions to provide feedback to the users about their learning performance. Data from different sources were stored using the xAPI standard into a cloud-based Learning Record Store. The participants of the experiment were asked to rate their learning experience through an Activity Rating Tool indicating their perceived level of productivity, stress, challenge and abilities. These self-reported performance indicators were used as markers to train a Linear Mixed Effect Model to generate learner-specific predictions of the learning performance. We discuss the advantages and the limitations of the used approach, highlighting further development points.

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Investigating collaborative learning success with physiological coupling indices based on electrodermal activity

Cited by 57 publications

References 44 publications

Widget, Widget on the Wall, Am I Performing Well at All?

Widget, Widget on the Wall, Am I Performing Well at All?

From signals to knowledge: A conceptual model for multimodal learning analytics

Learning pulse

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